title: Monitoring tigers with confidence.
abstract: With only 5% of the world's wild tigers (Panthera tigris Linnaeus, 1758) remaining since the last century, conservationists urgently need to know whether or not the management strategies currently being employed are effectively protecting these tigers. This knowledge is contingent on the ability to reliably monitor tiger populations, or subsets, over space and time. In the this paper, we focus on the 2 seminal methodologies (camera trap and occupancy surveys) that have enabled the monitoring of tiger populations with greater confidence. Specifically, we: (i) describe their statistical theory and application in the field; (ii) discuss issues associated with their survey designs and state variable modeling; and, (iii) discuss their future directions. These methods have had an unprecedented influence on increasing statistical rigor within tiger surveys and, also, surveys of other carnivore species. Nevertheless, only 2 published camera trap studies have gone beyond single baseline assessments and actually monitored population trends. For low density tiger populations (e.g. <1 adult tiger/100 km(2)) obtaining sufficient precision for state variable estimates from camera trapping remains a challenge because of insufficient detection probabilities and/or sample sizes. Occupancy surveys have overcome this problem by redefining the sampling unit (e.g. grid cells and not individual tigers). Current research is focusing on developing spatially explicit capture-mark-recapture models and estimating abundance indices from landscape-scale occupancy surveys, as well as the use of genetic information for identifying and monitoring tigers. The widespread application of these monitoring methods in the field now enables complementary studies on the impact of the different threats to tiger populations and their response to varying management intervention.
authors.affiliation: Fauna & Flora International, Cambridge, UKNational Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UKPanthera, New York, NY, USAWWF Malaysia, Selangor, MalaysiaDurrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK.
authors.forename: Matthew
authors.initials: M
authors.lastname: Linkie
authors.affiliation: Fauna & Flora International, Cambridge, UKNational Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UKPanthera, New York, NY, USAWWF Malaysia, Selangor, MalaysiaDurrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK.
authors.forename: Gurutzeta
authors.initials: G
authors.lastname: Guillera-Arroita
authors.affiliation: Fauna & Flora International, Cambridge, UKNational Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UKPanthera, New York, NY, USAWWF Malaysia, Selangor, MalaysiaDurrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK.
authors.forename: Joseph
authors.initials: J
authors.lastname: Smith
authors.affiliation: Fauna & Flora International, Cambridge, UKNational Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UKPanthera, New York, NY, USAWWF Malaysia, Selangor, MalaysiaDurrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK.
authors.forename: D Mark
authors.initials: DM
authors.lastname: Rayan
mesh_terms: D000818:Animals; D003247:Conservation of Natural Resources / Q000379:methods*; D056727:Endangered Species; D010781:Photography; D011157:Population Dynamics; D046010:Tigers / Q000502:physiology*